---
title: "Comparison: TensorZero vs. Portkey"
sidebarTitle: "Portkey"
description: "TensorZero is an open-source alternative to Portkey featuring an LLM gateway, observability, optimization, evaluations, and experimentation."
---

TensorZero and Portkey offer diverse features to streamline LLM engineering, including an LLM gateway, observability tools, and more.
TensorZero is fully open-source and self-hosted, while Portkey offers an open-source gateway but otherwise requires a paid commercial (hosted) service.
Additionally, TensorZero has more features around LLM optimization (e.g. advanced fine-tuning workflows and inference-time optimizations), whereas Portkey has a broader set of features around the UI (e.g. prompt playground).

## Similarities

- **Unified Inference API.**
  Both TensorZero and Portkey offer a unified inference API that allows you to access LLMs from most major model providers with a single integration, with support for structured outputs, batch inference, tool use, streaming, and more.<br />
  [→ TensorZero Gateway Quickstart](/quickstart/)

- **Automatic Fallbacks, Retries, & Load Balancing for Higher Reliability.**
  Both TensorZero and Portkey offer automatic fallbacks, retries, and load balancing features to increase reliability.<br />
  [→ Retries & Fallbacks with TensorZero](/gateway/guides/retries-fallbacks/)

- **Schemas, Templates.**
  Both TensorZero and Portkey offer schema and template features to help you manage your LLM applications.<br />
  [→ Prompt Templates & Schemas with TensorZero](/gateway/create-a-prompt-template)

- **Multimodal Inference.**
  Both TensorZero and Portkey support multimodal inference.<br />
  [→ Multimodal Inference with TensorZero](/gateway/guides/multimodal-inference/)

## Key Differences

### TensorZero

- **Open-Source Observability.**
  TensorZero offers built-in open-source observability features, collecting inference and feedback data in your own database.
  Portkey also offers observability features, but they are limited to their commercial (hosted) offering.

- **Built-in Evaluations.**
  TensorZero offers built-in evaluation functionality, including heuristics and LLM judges.
  Portkey doesn't offer any evaluation features.<br />
  [→ TensorZero Evaluations Overview](/evaluations/)

- **Open-Source Inference Caching.**
  TensorZero offers open-source inference caching features, allowing you to cache requests to improve latency and reduce costs.
  Portkey also offers inference caching features, but they are limited to their commercial (hosted) offering.<br />
  [→ Inference Caching with TensorZero](/gateway/guides/inference-caching/)

- **Open-Source Fine-Tuning Workflows.**
  TensorZero offers open-source built-in fine-tuning workflows, allowing you to create custom models using your own data.
  Portkey also offers fine-tuning features, but they are limited to their enterprise ($$$) offering.<br />
  [→ Fine-Tuning Recipes with TensorZero](/recipes/)

- **Advanced Fine-Tuning Workflows.**
  TensorZero offers advanced fine-tuning workflows, including the ability to curate datasets using feedback signals (e.g. production metrics) and the ability to use RLHF for reinforcement learning.
  Portkey doesn't offer similar features.<br />
  [→ Fine-Tuning Recipes with TensorZero](/recipes/)

- **Automated Experimentation (A/B Testing).**
  TensorZero offers advanced A/B testing features, including automated experimentation, to help your identify the best models and prompts for your use cases.
  Portkey only offers simple canary and A/B testing features.<br />
  [→ Run adaptive A/B tests with TensorZero](/experimentation/run-adaptive-ab-tests/)

- **Inference-Time Optimizations.**
  TensorZero offers built-in inference-time optimizations (e.g. dynamic in-context learning), allowing you to optimize your inference performance.
  Portkey doesn't offer any inference-time optimizations.<br />
  [→ Inference-Time Optimizations with TensorZero](/gateway/guides/inference-time-optimizations/)

- **Programmatic & GitOps-Friendly Orchestration.**
  TensorZero can be fully orchestrated programmatically in a GitOps-friendly way.
  Portkey can manage some of its features programmatically, but certain features depend on its external commercial hosted service.

- **Open-Source Access Control.**
  Both TensorZero and Portkey offer access control features like TensorZero API keys.
  Portkey only offers them in the commercial (hosted) offering, whereas TensorZero's solution is fully open-source.<br />
  [→ Set up auth for TensorZero](/operations/set-up-auth-for-tensorzero)

### Portkey

- **Prompt Playground.**
  Portkey offers a prompt playground in its commercial (hosted) offering, allowing you to test your prompts and models in a graphical interface.
  TensorZero doesn't offer a prompt playground today (coming soon!).

- **Guardrails.**
  Portkey offers guardrails features, including integrations with third-party guardrails providers and the ability to use custom guardrails using webhooks.
  For now, TensorZero doesn't offer built-in guardrails, and instead requires you to manage integrations yourself.

- **Managed Service.**
  Portkey offers a paid managed (hosted) service in addition to the open-source version.
  TensorZero is fully open-source and self-hosted.
